scholarly journals Using the Fire Weather Index (FWI) to improve the estimation of fire emissions from fire radiative power (FRP) observations

2018 ◽  
Vol 18 (8) ◽  
pp. 5359-5370 ◽  
Author(s):  
Francesca Di Giuseppe ◽  
Samuel Rémy ◽  
Florian Pappenberger ◽  
Fredrik Wetterhall
Keyword(s):  
Biomass Burning ◽  
Atmospheric Composition ◽  
Composition Analysis ◽  
Fire Weather ◽  
Atmospheric Conditions ◽  
Weather Index ◽  
Fire Weather Index ◽  
Fire Emissions ◽  
Radiative Power ◽  
Fire Radiative Power

Abstract. The atmospheric composition analysis and forecast for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass-burning fire emission estimates from the Global Fire Assimilation System (GFAS). The GFAS is a global system and converts fire radiative power (FRP) observations from MODIS satellites into smoke constituents. Missing observations are filled in using persistence, whereby observed FRP values from the previous day are progressed in time until a new observation is recorded. One of the consequences of this assumption is an increase of fire duration, which in turn translates into an increase of emissions estimated from fires compared to what is available from observations. In this study persistence is replaced by modelled predictions using the Canadian Fire Weather Index (FWI), which describes how atmospheric conditions affect the vegetation moisture content and ultimately fire duration. The skill in predicting emissions from biomass burning is improved with the new technique, which indicates that using an FWI-based model to infer emissions from FRP is better than persistence when observations are not available.

scholarly journals Combining fire radiative power observations with the fire weather index improves the estimation of fire emissions

2017 ◽  
Author(s):  
Francesca Di Giuseppe ◽  
Samuel Rémy ◽  
Florian Pappenberger ◽  
Fredrik Wetterhall
Keyword(s):  
Biomass Burning ◽  
Atmospheric Composition ◽  
Composition Analysis ◽  
Fire Weather ◽  
Atmospheric Conditions ◽  
Weather Index ◽  
Fire Weather Index ◽  
Fire Emissions ◽  
Radiative Power ◽  
Fire Radiative Power

Abstract. The atmospheric composition analysis and forecast for the European Copernicus Atmosphere Monitoring Services (CAMS) relies on biomass burning fire emission estimates from the Global Fire Assimilation System (GFAS). GFAS converts fire radiative power (FRP) observations from MODIS satellites into smoke constituents. Missing observations are filled in using persistence where observed FRP from the previous day are progressed in time until a new observation is recorded. One of the consequences of this assumption is an overestimation of fire duration, which in turn translates into an overestimation of emissions from fires. In this study persistence is replaced by modelled predictions using the Canadian Fire Weather Index (FWI), which describes how atmospheric conditions affect the vegetation moisture content and ultimately fire duration. The skill in predicting emissions from biomass burning is improved with the new technique, which indicates that using an FWI-based model to infer emissions from FRP is better than persistence when observations are not available.

Improving Forecasts of Biomass Burning Emissions with the Fire Weather Index

2017 ◽  
Vol 56 (10) ◽  
pp. 2789-2799 ◽  
Author(s):  
Francesca Di Giuseppe ◽  
Samuel Rémy ◽  
Florian Pappenberger ◽  
Fredrik Wetterhall
Keyword(s):  
Biomass Burning ◽  
Atmospheric Composition ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Daily Change ◽  
Global Statistics ◽  
In Fire ◽  
The Impact ◽  
Dependent Factor

AbstractIn the absence of a dynamical fire model that could link the emissions to the weather dynamics and the availability of fuel, atmospheric composition models, such as the European Copernicus Atmosphere Monitoring Services (CAMS), often assume persistence, meaning that constituents produced by the biomass burning process during the first day are assumed constant for the whole length of the forecast integration (5 days for CAMS). While this assumption is simple and practical, it can produce unrealistic predictions of aerosol concentration due to an excessive contribution from biomass burning. This paper introduces a time-dependent factor , which modulates the amount of aerosol emitted from fires during the forecast. The factor is related to the daily change in fire danger conditions and is a function of the fire weather index (FWI). The impact of the new scheme was tested in the atmospheric composition model managed by the CAMS. Experiments from 5 months of daily forecasts in 2015 allowed for both the derivation of global statistics and the analysis of two big fire events in Indonesia and Alaska, with extremely different burning characteristics. The results indicate that time-modulated emissions based on the FWI calculations lead to predictions that are in better agreement with observations.

scholarly journals Hindcast experiments of tropospheric composition during the summer 2010 fires over Western Russia

2011 ◽  
Vol 11 (12) ◽  
pp. 31851-31909 ◽  
Author(s):  
V. Huijnen ◽  
J. Flemming ◽  
J. W. Kaiser ◽  
A. Inness ◽  
J. Leitão ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Quantitative Description ◽  
Atmospheric Composition ◽  
Fire Emissions ◽  
Radiative Power ◽  
The Impact ◽  
Assimilation System ◽  
Fire Radiative Power ◽  
Satellite Instruments

Abstract. The severe wildfires in Western Russia during July–August 2010 coincided with a strong heat wave and led to large emissions of aerosols and trace gases such as carbon monoxide (CO), hydrocarbons and nitrogen oxides into the troposphere. This extreme event is used to evaluate the ability of the global MACC (Monitoring Atmospheric Composition and Climate) atmospheric composition forecasting system to analyze large-scale pollution episodes and to test the respective influence of a priori emission information and data assimilation on the results. Daily 4-day hindcasts were conducted using assimilated aerosol optical depth (AOD), CO, nitrogen dioxide (NO2) and ozone (O3) data from a range of satellite instruments. Daily fire emissions were used from the Global Fire Assimilation System (GFAS) version 1.0, derived from satellite fire radiative power retrievals. The impact of accurate wildfire emissions is dominant on the composition in the boundary layer, whereas the assimilation system influences concentrations throughout the troposphere, reflecting the vertical sensitivity of the satellite instruments. The application of the daily fire emissions reduces the area-average mean bias by 63% (for CO), 38% (O3) and 64% (NO2) during the first 24 h, compared to a reference simulation with a multi-annual mean climatology of biomass burning emissions. When initial tracer concentrations are further constrained by data assimilation, biases are reduced by 87, 38 and 80%. The forecast accuracy, quantified by the mean bias up to 96 h lead time, was best for all compounds when using both the GFAS emissions and assimilation. The model simulations suggest an indirect positive impact of O3 and CO assimilation on hindcasts of NO2 via changes in the oxidizing capacity. However, the quality of local hindcasts was strongly depending on the assumptions made for forecasted fire emissions. This was well visible from a relatively rapid increase by the root mean square error with respect to ground-based data for AOD, and satellite based NO2. This calls for a more advanced method to forecast fire emissions than the currently adopted persistency approach. The combined analysis of fire radiative power observations, multiple trace gas and aerosol satellite observations, as provided by the MACC system, results in a detailed quantitative description of the impact of major fires on atmospheric composition, and demonstrate the capabilities for the real-time analysis and forecasts of large-scale fire events.

scholarly journals Daily global fire radiative power fields estimation from one or two MODIS instruments

2014 ◽  
Vol 14 (24) ◽  
pp. 13377-13390 ◽  
Author(s):  
S. Remy ◽  
J. W. Kaiser
Keyword(s):  
Dry Matter ◽  
Low Earth Orbit ◽  
Atmospheric Composition ◽  
Correction Algorithm ◽  
Fire Cycle ◽  
Fire Emissions ◽  
Radiative Power ◽  
Leo Satellite ◽  
Linear Regressions ◽  
Fire Radiative Power

Abstract. Fires are important emitters of aerosol and trace gases and as such need to be taken into account in any atmospheric composition modelling enterprise. One method to estimate these emissions is to convert fire radiative power (FRP) analysis into dry matter burnt and emissions of smoke constituents using land-cover-dependent conversion factors. Inventories like the Global Fire Assimilation System (GFAS) follow this approach by calculating daily global smoke emissions from FRP observed by the MODIS instruments onboard the Terra and Aqua satellites. Observations with different overpass times systematically sample fires at different stages in the strong diurnal fire cycle. For some time periods, observations are available from only one instrument, which leads to a bias in the observed average FRP. We develop a method to correct this bias in daily FRP observations from any low Earth orbit (LEO) satellite, so that the budget of daily smoke emissions remains independent of the number of satellites from which FRP observations are taken into account. This ensures the possibility of running, for example, GFAS in case of failure of one of the MODIS instruments. It also enables the extension GFAS to 2000–2002 and the inclusion of FRP observations from upcoming satellite missions. The correction combines linear and non-linear regressions and uses an adaptive regionalization algorithm. It decreases the bias in daily average FRP from Terra and Aqua by more than 95%, and RMSE by 75% for Aqua and 55% for Terra. The correction algorithm is applied to Terra observations from 25 February 2000 to 31 December 2002, when Aqua observations were not available. The database of fire emissions GFASv1.0 is extended correspondingly.

scholarly journals Hindcast experiments of tropospheric composition during the summer 2010 fires over western Russia

2012 ◽  
Vol 12 (9) ◽  
pp. 4341-4364 ◽  
Author(s):  
V. Huijnen ◽  
J. Flemming ◽  
J. W. Kaiser ◽  
A. Inness ◽  
J. Leitão ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Forecast Accuracy ◽  
Satellite Observations ◽  
Atmospheric Composition ◽  
Fire Emissions ◽  
Radiative Power ◽  
The Impact ◽  
Assimilation System ◽  
Fire Radiative Power

Abstract. The severe wildfires in western Russia during July–August 2010 coincided with a strong heat wave and led to large emissions of aerosols and trace gases such as carbon monoxide (CO), hydrocarbons and nitrogen oxides into the troposphere. This extreme event is used to evaluate the ability of the global MACC (Monitoring Atmospheric Composition and Climate) atmospheric composition forecasting system to provide analyses of large-scale pollution episodes and to test the respective influence of a priori emission information and data assimilation on the results. Daily 4-day hindcasts were conducted using assimilated aerosol optical depth (AOD), CO, nitrogen dioxide (NO2) and ozone (O3) data from a range of satellite instruments. Daily fire emissions were used from the Global Fire Assimilation System (GFAS) version 1.0, derived from satellite fire radiative power retrievals. The impact of accurate wildfire emissions is dominant on the composition in the boundary layer, whereas the assimilation system influences concentrations throughout the troposphere, reflecting the vertical sensitivity of the satellite instruments. The application of the daily fire emissions reduces the area-average mean bias by 63% (for CO), 60% (O3) and 75% (NO2) during the first 24 h with respect to independent satellite observations, compared to a reference simulation with a multi-annual mean climatology of biomass burning emissions. When initial tracer concentrations are further constrained by data assimilation, biases are reduced by 87, 67 and 90%. The forecast accuracy, quantified by the mean bias up to 96 h lead time, was best for all compounds when using both the GFAS emissions and assimilation. The model simulations suggest an indirect positive impact of O3 and CO assimilation on hindcasts of NO2 via changes in the oxidizing capacity. However, the quality of local hindcasts was strongly dependent on the assumptions made for forecasted fire emissions. This was well visible from a relatively poor forecast accuracy quantified by the root mean square error, as well as the temporal correlation with respect to ground-based CO total column data and AOD. This calls for a more advanced method to forecast fire emissions than the currently adopted persistency approach. The combined analysis of fire radiative power observations, multiple trace gas and aerosol satellite observations, as provided by the MACC system, results in a detailed quantitative description of the impact of major fires on atmospheric composition, and demonstrate the capabilities for the real-time analysis and forecasts of large-scale fire events.

scholarly journals Daily global fire radiative power fields estimation from one or two MODIS instruments

2014 ◽  
Vol 14 (14) ◽  
pp. 20805-20844 ◽  
Author(s):  
S. Remy ◽  
J. W. Kaiser
Keyword(s):  
Global Scale ◽  
Low Earth Orbit ◽  
Atmospheric Composition ◽  
Correction Algorithm ◽  
Fire Cycle ◽  
Fire Emissions ◽  
Radiative Power ◽  
Leo Satellite ◽  
Linear Regressions ◽  
Fire Radiative Power

Abstract. Fires are important emitters of aerosol and trace gases and as such need to be taken into account in any atmospheric composition modeling enterprise. One method to estimate these emissions is to convert Fire Radiative Power (FRP) analysis to dry matter burnt and emissions of smoke constituents using land cover dependent conversion factors. Inventories like the Global Fire Assimilation System (GFAS) follow this approach by calculating daily global smoke emissions from FRP observed by the MODIS instruments on-board of the Terra and Aqua satellites. Observations with different overpass times systematically sample fires at different stages in the strong diurnal fire cycle. For some time periods, observations are available from only one instrument, which leads to a bias in the observed average FRP. We develop a method to correct this bias in daily FRP observations from any Low Earth Orbit (LEO) satellite, so that the budget of daily smoke emissions remains independent of the number of satellites from which FRP observations are taken into account. This ensures the possibility of running, e.g., GFAS in case of a default of one of the MODIS instruments. It also enables the extension GFAS to 2000–2002 and the inclusion of FRP observations from upcoming satellite missions. The correction combines linear and non-linear regressions and uses an adaptive regionalization algorithm. It removes the bias in daily average FRP observations from Terra and Aqua nearly entirely. Errors are larger for Terra than for Aqua, are generally relatively small at a global scale, but can be important at a local scale. The correction algorithm is applied to Terra observations from 25 February 2000 to 31 December 2002, when Aqua observations were not available. The database of fire emissions GFASv1.0 is extended correspondingly.

Fire-Weather Index and Climate Change

2020 ◽  
pp. 45-63 ◽  
Author(s):  
Zuzana Hubnerova ◽  
Sylvia Esterby ◽  
Steve Taylor
Keyword(s):  
Climate Change ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index

scholarly journals Proposal of an Iot Solution to Fire Risk Assessment Problem

2020 ◽  
Author(s):  
Ana Bernardo ◽  
Pedro Silva ◽  
Paulo Fazendeiro
Keyword(s):  
Forest Fires ◽  
Fire Risk ◽  
Initial Response ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Keywords Internet ◽  
The Common ◽  
Civic Awareness ◽  
Municipal Level

Several of the fighting weaknesses evidenced by the forest fires tragedies of the last years are rooted in the disconnection between the current technical/scientific resources and the availability of the resulting information to operational agents on the ground. In order to be effective, a pre-emptive response to similar disasters must include the articulation between local authorities at municipal level - in prevention, preparedness and initial response - and the common citizen who is on the field, resides there, and has a deeper knowledge about the field of operation. This work intends to take a first step in the development of a tool that can serve to improve the civic awareness of all and to support the decision-making of the competent authorities. Keywords: Internet of things, Citizen science, Fire weather index

scholarly journals Increased trends in global extreme fire weather driven predominantly by atmospheric humidity and temperature

2021 ◽  
Author(s):  
Piyush Jain ◽  
Dante Castellanos-Acuna ◽  
Sean Coogan ◽  
John Abatzoglou ◽  
Mike Flannigan
Keyword(s):  
Global Analysis ◽  
Vapour Pressure Deficit ◽  
Fire Risk ◽  
Dew Point ◽  
Fire Weather ◽  
Atmospheric Humidity ◽  
Weather Index ◽  
Fire Weather Index ◽  
Temperature Trends ◽  
Extreme Fire

Abstract Climate and weather greatly influence wildfire, and recent increases in wildfire activity have been linked to climate change. However, the atmospheric drivers of observed changes have not been articulated globally. We present a global analysis of trends in extreme fire weather from 1979–2020. Significant increases in extreme (95th percentile) annual values of the Fire Weather Index (FWI95), Initial Spread Index (ISI95), and Vapour Pressure Deficit (VPD95) occurred over 26.0%, 26.1%, and 46.1% of the global burnable landmass, respectively. Significant trends corresponded to a 35.8%, 36.0%, and 21.4% increase in mean global FWI95, ISI95, and VPD95, respectively. Relative humidity and temperature were identified as the drivers of significant trends in FWI95 and ISI95 in most regions, largely where temperature trends outpaced dew point trends. We identified relatively few regions in which wind speed or precipitation were drivers. These findings have wide-ranging implications for understanding fire risk in a changing climate.

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